The cost of feeding is the single largest variable cost in beef production systems, accounting for approximately 70% of the total production cost (Perry and Cecava, 1995, Beef Cattle Feeding and Nutrition, 2nd Ed., Academic Press, San Diego, Calif.). Generally, about 70-75% of the total dietary energy consumed in a beef production system is used for maintenance (Ferrell and Jenkins 1984, J. Anim. Sci. 58:234-243; NRC (National Research Council) 1996, Nutrient Requirements of Beef Cattle, Seventh Reviewed Edition, Washington, D.C.: National Academy Press). This means higher beef production costs, especially in large-sized breeding animals due to presumably higher maintenance energy needs, lower overall production system efficiency, and therefore lower profits. Indeed, compared to swine and poultry, which are able to convert about 14 and 22%, respectively, of the total energy intake into protein deposition, only 5% of the total energy intake in beef cattle is converted into deposited protein. Improvements in the efficiency of feed utilization by beef cattle would therefore lead to better economic returns from both beef cattle breeding operations and feedlots (Gibb and McAllister, 1999, “The impact of feed intake and feeding behaviour of cattle on feedlot and feedbunk management,” Pages 101-116. D. Korver and J. Morrison (ed). Proc. 20th Western Nutr. Conf. Canada; Liu et al., 2000, Can. J. Anim. Sci. 80:435-441; Herd et al., 2003, J. Anim. Sci. 81(E. Suppl. 1):E9-E17). According to Johnson et al. (2003) J. Anim. Sci. 2003. 81:E27-E38, the reasons for the lack of change in beef cattle energetic efficiency, despite several years of intensive production, include the lack of a consistent selection goal, loose and inconsistent definitions of efficiency, concentration on output traits, and emphasis on population similarities rather than individual variation.
Efficient beef cattle production involves a complex summation of appropriate levels of available feed inputs and product outputs over a range of different production systems involving animals at different developmental stages. Thus, several indices have been proposed for determining the energetic efficiency of beef production, as comprehensively reviewed by Archer, et al. (1999) Australian Journal of Agricultural Research 50:147-161. These include feed conversion ratio (FCR), maintenance efficiency, partial efficiency of growth (PEG), cow-calf efficiency, and residual feed intake (RFI). Two other indices are relative growth rate (growth relative to instantaneous body size) and Kleiber ratio (weight gain per unit metabolic body size).
Traditionally, feed efficiency has been expressed in terms of FCR, or its inverse (gross feed efficiency, GFE). This is usually measured as the ratio of feed consumed to gain in weight. It reflects the efficiency of use of the energy consumed for maintenance and growth and captures the relationship between input of feed and output of product (Herd et al., 2003, supra). Though FCR has been in existence for many years, it is difficult to improve through direct selection because it is difficult to measure on the individual and its genetic correlation with growth rate implies that selection for it can lead to an increase in body weight (BW) and feed intake, which is not always desirable (Gunsett, 1984, J. Anim. Sci. 59: 1185-1193; Archer et al., 1999, supra; Crews, 2005, Genet. Mol. Res. 4 (2): 152-165). On the other hand, several studies in different species have demonstrated considerable phenotypic and genetic variations among individual animals in feed intake above and below the predicted requirements for maintenance and growth (Foster et al., 1983, Anim. Prod. 37:387-393; Luiting and Urff, 1991, Poult. Sci. 70:1663-1672; Archer et al., 1998, Anim. Sci. 67:171-182; Archer et al., 1999, supra). This variation in intake is usually measured as RFI, and was first proposed for use in cattle by Koch et al. (1963) J. Anim. Sci. 22:486-494.
Ultimately, the resulting phenotypic information collected using automated feed intake monitoring systems could be employed to dissect the molecular architecture of several economically relevant, but complex traits (ERT) in beef cattle. Molecular techniques can be employed to detect and map the chromosomal locations of genes contributing to variation in growth, feed intake, energetic efficiency, feeding behavior, and carcass merit. Several molecular tools and approaches, as well as statistical and computational techniques, are available that can be employed to quantify the number(s), location(s) and effect(s) of quantitative trait loci (QTL) through the use of genotypic information from genetic markers that are evenly spaced along chromosomes in the genome. A QTL is defined as the chromosomal location of individual or groups of genes, of unknown primary function, that show(s) significant association with a complex trait of interest (Lander and Kruglyuak, 1995, Natural Genet 11: 241-247). In beef cattle, QTL have been detected for disease tolerance (Hanotte et al., 2003, PNAS Agricultural Sciences 100:7443-7448), fertility and reproductive performance (Kirkpatrick et al., 2000, Mammalian Genome 11:136-139), body conformation (Grobet et al., 1998, Mammalian Genome 9: 210-213), birth weight and growth performance (Davis et al., 1998, Proc. 6th World Congr. Genet. Appl. Livest. Prod. 23: 441-444; Casas et al., 2003, J. Anim. Sci. 81, 2976-83; Li et al., 2002, J. Anim. Sci. 80:1187-1194; Kim et al., 2003, J. Anim. Sci 81, 1933-42), and carcass and meat quality (Keele et al., 1999, J. Anim. Sci 77, 1364-1371; Casas et al., 2000, J. Anim. Sci. 78:560-569; MacNeil and Grosz, 2002, J. Anim. Sci. 80:2316-2324; Casas et al., 2003, supra; Kim et al., 2003, supra; Moore et al., 2003, J. Anim. Sci. 81:1919-1925; and Li et al., 2004, J. Anim Sci. 2004 82: 967-972).
It is possible to search for and identify associations between polymorphisms in specific candidate genes and measures of variation in feed intake, feed efficiency and feeding behavior. A candidate gene may be selected based on previously known biochemical or physiological information or may be chosen because it maps to or close to the location of a QTL (positional candidate gene). Of interest among these candidates are genes shown to affect feed intake, behavior, energy balance, and body composition, such as the appetite regulating gene leptin. Several polymorphisms in candidate genes have been shown to be associated with economically relevant traits in cattle (Chrenek et al., 1998, Czech Journal of Animal Science 43, 541-544; Barendse et al., 2001, “The TG5 DNA marker test for marbling capacity in Australian feedlot cattle,” on the worldwide web at beef.crc.org.au/Publications/MarblingSym/Day1/Tg5DNA; Ge et al., 2001, J. Anim. Sci. 79:1757-1762; Grisart et al., 2002, Genome Research 12:222-231; Buchanan et al., 2002; Genet. Sel. Evol. 34:105-116; Moore et al., 2003, J. Anim. Sci. 81:1919-1925; Li et al., 2004, supra; and Nkrumah et al., 2005, J. Anim. Sci. 83:20-28).
The bovine microsatellite ETH10, located on bovine chromosome 5, has recently been associated with marbling (deposition of intramuscular fat) in Asian breeds of cattle (Smith et al. 2001, J. Animal Sci 79:3041-51; and U.S. Pat. No. 6,383,751 (“the '751 patent”)). The '751 patent suggests that differences in marbling score, between related cattle with different ETH10 genotypes, is likely due to a closely linked gene. The '751 patent proposes that retinol dehydrogenase (11-cis and 9-cis) (RDH5), which maps 1.01 centi-rads (cR) from ETH10 on the bovine radiation hybrid map (Womack et al. 1997, Mamm Genome 8:854-6), was the responsible gene. The association between ETH10 and marbling was highly significant with a P-value of <0.00015. Even though strong linkage disequilibrium would exist in the population tested, a P-value of this magnitude suggests that the gene responsible might be more closely linked to ETH 10 than RDH5.
Using a bioinformatics-based method to identify sequence homologies between bovine microsatellites and gene sequences from other species, it was demonstrated that ETH10 was putatively located within the 5′ UTR of the bovine STAT6 gene (Farber and Medrano, 2003, Animal Genetics 34:11-18). To support the location of ETH10, a bovine sequence tagged site (STS) in the 3′ UTR of bovine STAT6 was designed from available EST sequences.
STAT6 is the principal transcription factor involved in interlukin-4 (IL-4) and IL-13 signaling (Takeda et al. 1997, J Mol Med 75:317-326). In this context, a polymorphic microsatellite (homologous to ETH10) in the first exon of human STAT6 has been associated with predisposition to allergic diseases, due to altered IL-4 and IL-13 signal transduction (Tamura et al. 2001, Clinical and Experimental Allergy 31:1509-1514). More importantly, STAT6 has been shown to be activated by the full length form and not the truncated form of the leptin receptor in cell culture, implicating it as a potential mediator of the anti-obesity effects of leptin (Ghilardi et al. 1996, Proc Natl Acad Sci USA 93:6231-6235). As a mediator of leptin signaling, different allelic forms of STAT6 could impact the level of circulating leptin, which would have a direct impact on the mass of adipocytes (Maffei et al. 1995, Nature Med 1:1155-61).
The present invention provides SNPs within the STAT6 gene that are correlated with economically important feedlot and carcass traits in livestock animals.